{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setting graph specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "global graph_opts1 ///\n",
    "\ttitle(, justification(left) color(black) span pos(11)) ///\n",
    "\tgraphregion(color(white) lc(white) la(center)) /// <- remove la(center) for Stata < 15\n",
    "\tylab(,angle(0) nogrid)  ///\n",
    "\tyscale(noline) legend(region(lc(none) fc(none)))\n",
    "\n",
    "\n",
    "global pct `\" 0 \"0%\" .25 \"25%\" .5 \"50%\" .75 \"75%\" 1 \"100%\" \"'\n",
    "\n",
    "*set matsize 5000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Uploading data (.dta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "use \"https://github.com/SaoriIwa/Stata-IE-Visual-Library/raw/master/Library/Bar%20plots/Horizontal%20stack%20bar%20plot/data.dta\", clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Ensuring that there is no memory under the name ``theResults``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap mat drop theResults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Creating a matrix and creating variable from the matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\tlocal x = 0\n",
    "\tqui foreach var of varlist ///\n",
    "\t\tcorrect treat_cxr re_3 re_4 treat_refer ///\n",
    "\t\tmed_any med_l_any_1 med_l_any_2 med_l_any_3 med_k_any_9 {\n",
    "\n",
    "\t\tmean `var' [pweight = weight_city]\n",
    "\t\tmat a = e(b)\n",
    "\t\tlocal mean = a[1,1]\n",
    "\t\tlocal mean = string(round(100*`mean',0))\n",
    "\t\tlocal mean = substr(\"`mean'\",1,strpos(\"`mean'\",\".\")+1)\n",
    "\n",
    "\t\tlocal ++x\n",
    "\t\tlocal theLabel : var label `var'\n",
    "\t\tlocal theLabels `\" `theLabels' `x' \"`theLabel'\" \"' // [`mean'%]\n",
    "\n",
    "\t\tcap mat drop theResult\n",
    "\t\treg `var' i.city [pweight = weight_city]\n",
    "\t\t\tlocal theR21 = `e(r2)'\n",
    "\t\t\tmat theResult = nullmat(theResult) , [`theR21']\n",
    "\n",
    "\t\treg `var' i.city i.case [pweight = weight_city]\n",
    "\t\t\tlocal theR22 = `e(r2)' - `theR21'\n",
    "\t\t\tmat theResult = nullmat(theResult) , [`theR22']\n",
    "\n",
    "\t\treg `var' i.city i.case i.type_formal  [pweight = weight_city]\n",
    "\t\t\tlocal theR23 = `e(r2)' - `theR21' - `theR22'\n",
    "\t\t\tmat theResult = nullmat(theResult) , [`theR23']\n",
    "\n",
    "\t\treg `var' i.city i.case i.type_formal i.sp_city_id  [pweight = weight_city]\n",
    "\t\t\tlocal theR24 = `e(r2)' - `theR21' - `theR22' - `theR23'\n",
    "\t\t\tmat theResult = nullmat(theResult) , [`theR24']\n",
    "\n",
    "\t\treg `var' i.city i.case i.type_formal i.sp_city_id i.sp_city_mbbs [pweight = weight_city]\n",
    "\t\t\tlocal theR25 = `e(r2)' - `theR21' - `theR22' - `theR23' - `theR24'\n",
    "\t\t\tmat theResult = nullmat(theResult) , [`theR25']\n",
    "\n",
    "\t\tmean `var' [pweight = weight_city]\n",
    "\t\tmat a = e(b)\n",
    "\t\tlocal mean = a[1,1]\n",
    "\t\tmat theResult = nullmat(theResult) , [`mean']\n",
    "\n",
    "\t\t\tmat theResults = nullmat(theResults) \\ theResult\n",
    "\t\t\tmatlist theResults\n",
    "\n",
    "\t\t\t}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear\n",
    "qui svmat theResults\n",
    "qui gen n = _n\n",
    "\n",
    "label def n `theLabels'\n",
    "label val n n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Creating the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "This front-end cannot display the desired image type."
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph bar (sum) theResults1 theResults2 theResults3 theResults4 theResults5  ///\n",
    "\t, ylab($pct) $graph_opts1 hor stack over(n) xsize(6) ///\n",
    "\tbar(1, lc(black) lw(thin)) ///\n",
    "\tbar(2, lc(black) lw(thin)) ///\n",
    "\tbar(3, lc(black) lw(thin)) ///\n",
    "\tbar(4, fc(black) lc(black) lw(thin)) ///\n",
    "\tbar(5, fc(gs12) lc(black) lw(thin)) ///\n",
    "\tlegend(pos(5) ring(0) c(1) symxsize(small) symysize(small)  ///\n",
    "\torder(6 \"Variance Explained By:\" 1 \"City Setting\" 2 \"Case Scenario\" 3 \"MBBS Degree\" 4 \"All SP Characteristics\" 5 \"Full Interaction Model\"  ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Exporting the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui graph export \"figure.png\" , replace width(1000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Stata",
   "language": "stata",
   "name": "stata"
  },
  "language_info": {
   "file_extension": ".do",
   "mimetype": "text/x-stata",
   "name": "stata"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
